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Showing 3 results for Bayes Estimator
Ahmad Parsian, Shahram Azizi Sazi,
Volume 2, Issue 1 (8-2008)
Abstract
In this paper, a new class of estimators namely Constrained Bayes Estimators are obtained under Balanced Loss Function (BLF) and Weighted Balanced Loss Function (WBLF) using a ``Bayesian solution". The Constrained Bayes Estimators are calculated for the natural parameter of one-parameter exponential families of distributions. A common approach to the prior uncertainty in Bayesian analysis is to choose a class $Gamma$ of prior distributions and look for an optimal decision within the class $Gamma$. This is known as robust Bayesian methodology. Among several methods of choosing the optimal rules in the context of the robust Bayes method, we discuss obtaining Posterior Regret Constrained Gamma-Minimax (PRCGM) rule under Squared Error Loss and then employing the ``Bayesian solution", we obtain the optimal rules under BLF and WBLF.
Shokofeh Zeinodini, Ahmad Parsian,
Volume 4, Issue 2 (3-2011)
Abstract
In this paper, a class of generalized Bayes Minimax estimators of the mean vector of a normal distribution with unknown positive definite covariance matrix is obtained under the sum of squared error loss function. It is shown that this class is an extension of the class obtained by Lin and Tasi (1973).
Akbar Asgharzadeh, Mina Azizpour, Reza Valiollahi,
Volume 9, Issue 1 (9-2015)
Abstract
One of the drawbacks of the type II progressive censoring scheme is that the length of the experiment can be very large. Because of that, recently a new censoring scheme named as the type II progressively hybrid censored scheme has received considerable interest among the statisticians. In this paper, the statistical inference for the half-logistic distribution is discussed based on the progressively type II hybrid censored samples. The maximum likelihood estimator, the approximate maximum likelihood estimator and the Bayes estimator of parameter using Lindley approximation and MCMC method are obtained. Asymptotic confidence intervals, Bootstrap confidence intervals and Bayesian credible intervals are obtained. Different point and interval estimators are compared using Monte Carlo simulation. A real data set is presented for illustrative purposes.
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